Why use R for plotting?

Why use R for plotting?

Why use R for plotting?

Beautiful and flexible graphics!

• Have you created plots?
  • What kind of plot?
  • Which software?
• Have you plotted in R?
  • base R, lattice?
  • ggplot2?

• To follow along:

Code and HTML available at http://qcbs.ca/wiki/r/workshop3

• Recommendation:
  • create your own new script
  • refer to provided code only if needed
  • avoid copy pasting or running the code directly from script
• ggplot2 is also hosted on github: https://github.com/hadley/ggplot2

The ggplot2 package lets you make beautiful and customizable plots of your data. It implements the grammar of graphics, an easy to use system for building plots.

Required packages

install.packages("ggplot2")
library(ggplot2)

1. Your first R plot
   • Basic scatter plot
   • Challenge 1
2. Grammar of graphics
   • More advanced plots
   • Available plot elements and when to use them
   • Challenge 2
3. Saving a plot
4. Fine tuning your plot
   • Colours
   • Themes
5. Miscellaneous

?qplot

arguments:

• data

• x

• y

• …

Look at pre-loaded "iris" dataset:

?iris
head(iris)
str(iris)
names(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

qplot(data = iris,
        x = Sepal.Length,
        y = Sepal.Width)

qplot(data = iris,
        x = Species,
        y = Sepal.Width)

arguments:

• xlab

• ylab

• main

qplot(data = iris,
        x = Sepal.Length, xlab = "Sepal Length (mm)",
        y = Sepal.Width, ylab = "Sepal Width (mm)",
        main = "Sepal dimensions")

Produce a basic plot with built in data (5 minutes)

?CO2
data(CO2)
?BOD
data(BOD)

qplot(data = CO2,
        x = conc, xlab = "CO2 Concentration (mL/L)",
        y = uptake, ylab = "CO2 Uptake (umol/m^2 sec)",
        main = "CO2 uptake in grass plants")

qplot(data = CO2, x = conc, y = uptake, 
      xlab = expression(paste(CO[2]," Concentration (ml/L)")),
      ylab = expression(paste(CO[2]," Uptake (", mu, "mol/", m^2," sec)")),
      main = expression(paste(CO[2]," uptake in grass plants")))

A graphic is made of elements (layers)

• Data
  • aesthetics (aes)
  • transformation
  • geoms (geometric objects)
  • axis (coordinate system)
  • scales

A graphic is made of elements (layers)

• Data
• Aesthetics (aes), to make data visible
  • x,y : position along the x and y axis
  • colour: the colour of the point
  • group: what group a point belongs to
  • shape: the figure used to plot a point
  • linetype: the type of line used (solid, dashed, etc)
  • size: the size of the point or line
  • alpha: the transparency of the point

A graphic is made of elements (layers)

• Data
• Aesthetics (aes)
• Geometric objects (geoms)
  • geom_point(): scatterplot
  • geom_line(): lines connecting points by increasing value of x
  • geom_path(): lines connecting points in sequence of appearance
  • geom_boxplot(): box and whiskers plot for categorical variables
  • geom_bar(): bar charts for categorical x axis
  • geom_histogram(): histogram for continuous x axis

Edit any single element to produce a new graph e.g., by changing the coordinate system

Create a simple plot object:

• plot.object <- ggplot() OR qplot()

Add graphical layers:

• plot.object <- plot.object + layer()

Repeat step 2 until statisfied, then print:

• print(plot.object)

qplot(data = iris,
      x = Sepal.Length,
      xlab = "Sepal Length (mm)",
      y = Sepal.Width,
      ylab = "Sepal Width (mm)",
      main = "Sepal dimensions")

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  xlab("Sepal Length (mm)") +
  ylab("Sepal Width (mm)") +
  ggtitle("Sepal dimensions")

basic.plot <- ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width)) +
    geom_point()+
    xlab("Sepal Length (mm)") + ylab("Sepal Width (mm)")+
    ggtitle("Sepal dimensions")
basic.plot

Add aesthetics using aes()

basic.plot <- basic.plot + aes(colour = Species, shape = Species)
basic.plot

Add linear regressions with geom_smooth()

linear.smooth.plot <- basic.plot + geom_smooth(method = "lm", se = F)
linear.smooth.plot

Produce a colourful plot with linear regression (or other smoother) from built in data such as the CO2 dataset or the msleep dataset

?CO2
data(CO2)
?msleep
data(msleep)

Example using loess smoothing

data(CO2)
CO2.plot <- ggplot(data = CO2, aes(x = conc, y = uptake, colour = Treatment)) +
    geom_point() +
    xlab("CO2 Concentration (mL/L)") +
    ylab("CO2 Uptake (umol/m^2 sec)") +    
    ggtitle("CO2 uptake in grass plants") +
    geom_smooth(method = "loess")
CO2.plot

Example using loess smoothing

Data becomes difficult to visualize when there are multiple factors, e.g., the CO2 data set contains data on CO2 uptake for chilled vs non-chilled treatments from two different regions. Let's build a basic plot using this data set:

CO2.plot <- ggplot(data = CO2, aes(x = conc, y = uptake, colour = Treatment)) +
    geom_point() +
    xlab("CO2 Concentration (mL/L)") +
    ylab("CO2 Uptake (umol/m^2 sec)") +    
    ggtitle("CO2 uptake in grass plants")
CO2.plot

If we want to compare regions, it is useful to make two panels. Synthax: plot.object + facet_grid(rows ~ columns)

CO2.plot <- CO2.plot + facet_grid(. ~ Type)
CO2.plot

Now that we have 2 facets, let's observe how the CO2 uptake evolves as CO2 concentrations rise, by adding connecting lines to the points using geom_line():

CO2.plot + geom_line()

Wrong! Because each treatment in each region has 3 replicates

Specify groups

CO2.plot <- CO2.plot + geom_line(aes(group = Plant))
CO2.plot

Data Visualization with ggplot2 Cheat Sheet

help(package = ggplot)

http://ggplot2.tidyverse.org/reference/

Explore a new geom and other plot elements with your own data or built in data.

data(msleep)
data(OrchardSprays)

data(OrchardSprays)
box.plot <- ggplot(data = OrchardSprays, aes(x=treatment, y=decrease)) +
  geom_boxplot()
box.plot

ggsave() will write directly to your working directory all in one line of code and you can specify the name of the file and the dimensions of the plot:

ggsave("CO2_plot.pdf",
       CO2.plot,
       height = 8.5,
       width = 11,
       units = "in")

Note that vector format (e.g., pdf, svg) are often preferable choice compared to raster format (jpeg, png, …)

Other methods to save image ?pdf ?jpeg

CO2.plot + 
  scale_colour_manual(values = c("nonchilled" = "red", "chilled" = "blue"))

install.packages("RColorBrewer")
require(RColorBrewer)

display.brewer.all()

CO2.plot + scale_color_brewer(palette = "Dark2")

Wes Anderson colour palette

install.packages("wesanderson")
library(wesanderson)

Wes Anderson colour palette

Wes Anderson colour palette

Wes Anderson colour palette

Wes Anderson colour palette

ggplot(data = iris, aes(Sepal.Length, Sepal.Width, color = Species)) + 
  geom_point(size = 3) + 
  scale_color_manual(values = wes_palette("GrandBudapest2")) 

Long data format has a column for possible variables and a column for the values of those variables

Wide data format has a separate column for each variable or each factor in your study

Wide data frame can be used for some basic plotting in ggplot2, but more complex plots require long format (example to come)

dplyr, lm(), glm(), gam() all require long data format

Tidying allows you to manipulate the structure of your data while preserving all original information

gather() - convert from wide to long format

spread() - convert from long to wide format

install.packages("tidyr")
library(tidyr)

(wide <- data.frame(Species = c("Oak", "Elm", "Ash"),
                   DBH = c(12, 20, 13), Height = c(56, 85, 55)))

##   Species DBH Height
## 1     Oak  12     56
## 2     Elm  20     85
## 3     Ash  13     55

gather(data, key, value, ...)

- data A data frame (e.g. wide)
- key name of the new column containing variable names (e.g. Measurement)
- value name of the new column containing variable values (e.g. Value)
- ... name or numeric index of the columns we wish to gather (e.g. DBH, Height)

wide

##   Species DBH Height
## 1     Oak  12     56
## 2     Elm  20     85
## 3     Ash  13     55

long = gather(wide, Measurement, Value, DBH, Height)
long

##   Species Measurement Value
## 1     Oak         DBH    12
## 2     Elm         DBH    20
## 3     Ash         DBH    13
## 4     Oak      Height    56
## 5     Elm      Height    85
## 6     Ash      Height    55

long

##   Species Measurement Value
## 1     Oak         DBH    12
## 2     Elm         DBH    20
## 3     Ash         DBH    13
## 4     Oak      Height    56
## 5     Elm      Height    85
## 6     Ash      Height    55

spread(data, key, value)

- data A data frame (e.g. long)
- key Name of the column containing variable names (e.g. Measurement)
- value Name of the column containing variable values (e.g. Value)

long

##   Species Measurement Value
## 1     Oak         DBH    12
## 2     Elm         DBH    20
## 3     Ash         DBH    13
## 4     Oak      Height    56
## 5     Elm      Height    85
## 6     Ash      Height    55

wide2 = spread(long, Measurement, Value)
wide2

##   Species DBH Height
## 1     Ash  13     55
## 2     Elm  20     85
## 3     Oak  12     56

1. Using the airquality dataset, gather all the columns (except Month and Day) into rows.

2. Then spread the resulting data frame to return to the original data format.

?airquality
data(airquality)

1. Using the airquality dataset, gather all the columns (except Month and Day) into rows.

air.long <- gather(airquality, variable, value, -Month, -Day)
head(air.long)

##   Month Day variable value
## 1     5   1    Ozone    41
## 2     5   2    Ozone    36
## 3     5   3    Ozone    12
## 4     5   4    Ozone    18
## 5     5   5    Ozone    NA
## 6     5   6    Ozone    28

Note that the syntax used here indicates that we wish to gather ALL the columns exept Month and Day. It is equivalent to: gather(airquality, value, Ozone, Solar.R, Temp, Wind)

1. Then spread the resulting data frame to return to the original data format.

air.wide <- spread(air.long, variable, value)
head(air.wide)

##   Month Day Ozone Solar.R Temp Wind
## 1     5   1    41     190   67  7.4
## 2     5   2    36     118   72  8.0
## 3     5   3    12     149   74 12.6
## 4     5   4    18     313   62 11.5
## 5     5   5    NA      NA   56 14.3
## 6     5   6    28      NA   66 14.9

separate() splits a columns by a character string separator

separate(data, col, into, sep)

• data A data frame (e.g. long)
  
• col Name of the column you wish to separate
• into Names of new variables to create
• sep Character which indicates where to separate

Create a fictional dataset about fish and plankton

set.seed(8)
messy <- data.frame(id = 1:4,
                    trt = sample(rep(c('control', 'farm'), each = 2)),
                    zooplankton.T1 = runif(4),
                    fish.T1 = runif(4),
                    zooplankton.T2 = runif(4),
                    fish.T2 = runif(4))
messy

##   id     trt zooplankton.T1    fish.T1 zooplankton.T2     fish.T2
## 1  1 control      0.3215092 0.76914695      0.4323914 0.001301721
## 2  2 control      0.7189275 0.64449114      0.5449621 0.264458864
## 3  3    farm      0.2908734 0.45704489      0.1382243 0.276532247
## 4  4    farm      0.9322698 0.08930101      0.9278123 0.521107042

First convert the messy data frame from wide to long format

messy.long <- gather(messy, taxa, count, -id, -trt)
head(messy.long)

##   id     trt           taxa     count
## 1  1 control zooplankton.T1 0.3215092
## 2  2 control zooplankton.T1 0.7189275
## 3  3    farm zooplankton.T1 0.2908734
## 4  4    farm zooplankton.T1 0.9322698
## 5  1 control        fish.T1 0.7691470
## 6  2 control        fish.T1 0.6444911

Then we want to split the 2 sampling time (T1 and T2).

messy.long.sep <- separate(messy.long, taxa, 
                           into = c("species", "time"), sep = "\\.") 
head(messy.long.sep)

##   id     trt     species time     count
## 1  1 control zooplankton   T1 0.3215092
## 2  2 control zooplankton   T1 0.7189275
## 3  3    farm zooplankton   T1 0.2908734
## 4  4    farm zooplankton   T1 0.9322698
## 5  1 control        fish   T1 0.7691470
## 6  2 control        fish   T1 0.6444911

The argument sep = "\\." tells R to splits the character string around the period (.). We cannot type directly "." because it is a regular expression that matches any single character.

A package that reshapes the layout of data sets.

Converting from wide to long format using gather()

Converting from long format to wide format using spread()

Split and merge columns with unite() and separate()

Data Wrangling with dplyr and tidyr Cheat Sheet

head(airquality)

##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

The dataset is in wide format, where measured variables (Ozone, Solar.R, Wind and Temp) are each in their own columns.

Let's use ggplot to visualize each individual variable and the range it displays for each month in the time series

fMonth <- factor(airquality$Month) 
# Convert the Month variable to a factor. 
 
ozone.box <- ggplot(airquality, aes(x = fMonth, y = Ozone)) + 
  geom_boxplot()

solar.box <- ggplot(airquality, aes(x = fMonth, y = Solar.R)) + 
  geom_boxplot()

temp.box  <- ggplot(airquality, aes(x = fMonth, y = Temp)) + 
  geom_boxplot()

wind.box  <- ggplot(airquality, aes(x = fMonth, y = Wind)) + 
  geom_boxplot()

You can use grid.arrange() in the package gridExtra to arrange the 4 separate plots into one panel for viewing.

combo.box <- grid.arrange(ozone.box, solar.box, temp.box, wind.box, 
                          nrow = 2) 
# nrow = number of rows you would like the plots displayed on

Convert airquality to long format to use facet_wrap() for the different variables as opposed to by month

air.long <- gather(airquality, variable, value, -Month, -Day)

fMonth.long <- factor(air.long$Month)

weather <- ggplot(air.long, aes(x = fMonth.long, y = value)) +
  geom_boxplot() +
  facet_wrap(~ variable, nrow = 2)

facet_wrap puts all the individual variables on the same scale, which can be useful in many cases. However, here, we can't see the variability in the Wind and Temp variables. We can free the y axis in each panel using scales=free

weather <- weather + 
  facet_wrap(~ variable, nrow = 2, scales = "free")

We can also use the long data format to create a plot with all the variables included on a single plot

w2 <- ggplot(air.long, aes(x = Day, y = value, colour = variable)) +
  geom_point() + # put all day measurements on one plot
  facet_wrap(~ Month, nrow = 1) #split observations by month

• Package that contains a set of functions (or “verbs”) for data manipulation such as filtering rows, selecting specific columns, re-ordering rows, adding new columns and summarizing data;
• Easy and intuitive functions
• Fast and efficient
• Can interface with external databases and translate your R code into SQL queries

Some corresponding R base functions: split(), subset(), apply(), sapply(), lapply(), tapply() and aggregate()

install.packages("dplyr")
library(dplyr)

These 4 core functions tackle the most common manipulations when working with data frames

• select(): select columns from a data frame
• filter(): filter rows according to defined criteria
• arrange(): re-order data based on criteria (e.g. ascending, descending)
• mutate(): create or transform values in a column

select(data, ...)

• ... Can be column names or positions or complex expressions separated by commas

select(data, column1, column2) select columns 1 and 2 select(data, c(2:4,6)) select columns 2 to 4 and 6 select(data, -column1) select all columns except column 1 select(data, start_with(x.)) select all columns that start with "x."

Example: suppose we are only interested in the variation of Ozone over time within the airquality dataset

ozone <- select(airquality, Ozone, Month, Day)
head(ozone)

##   Ozone Month Day
## 1    41     5   1
## 2    36     5   2
## 3    12     5   3
## 4    18     5   4
## 5    NA     5   5
## 6    28     5   6

Extract a subset of rows that meet one or more specific conditions

filter(dataframe, logical statement 1, logical statement 2, ...)

Example: we are interested in analyses that focus on the month of August during high temperature events

august <- filter(airquality, Month == 8, Temp >= 90)
# same as: filter(airquality, Month == 8 & Temp >= 90)
head(august)

##   Ozone Solar.R Wind Temp Month Day
## 1    89     229 10.3   90     8   8
## 2   110     207  8.0   90     8   9
## 3    NA     222  8.6   92     8  10
## 4    76     203  9.7   97     8  28
## 5   118     225  2.3   94     8  29
## 6    84     237  6.3   96     8  30

Re-order rows by a particular column, by default in ascending order

Use desc() for descending order.

arrange(data, variable1, desc(variable2), ...)

Example: 1. Let's use the following code to create a scrambled version of the airquality dataset

air_mess <- sample_frac(airquality, 1)
head(air_mess)

##     Ozone Solar.R Wind Temp Month Day
## 35     NA     186  9.2   84     6   4
## 63     49     248  9.2   85     7   2
## 93     39      83  6.9   81     8   1
## 33     NA     287  9.7   74     6   2
## 99    122     255  4.0   89     8   7
## 145    23      14  9.2   71     9  22

Example: 2. Now let's arrange the data frame back into chronological order, sorting by Month then Day

air_chron <- arrange(air_mess, Month, Day)
head(air_chron)

##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

Try : arrange(air_mess, Day, Month) and see the difference

Compute and add new columns

mutate(data, newVar1 = expression1, newVar2 = expression2, ...)

Example: we want to convert the temperature variable form degrees Fahrenheit to degrees Celsius

airquality_C <- mutate(airquality, Temp_C = (Temp-32)*(5/9))
head(airquality_C)

##   Ozone Solar.R Wind Temp Month Day   Temp_C
## 1    41     190  7.4   67     5   1 19.44444
## 2    36     118  8.0   72     5   2 22.22222
## 3    12     149 12.6   74     5   3 23.33333
## 4    18     313 11.5   62     5   4 16.66667
## 5    NA      NA 14.3   56     5   5 13.33333
## 6    28      NA 14.9   66     5   6 18.88889

Usually data manipulation require multiple steps, the magrittr package offers a pipe operator %>% which allows us to link multiple operations

install.packages("magrittr")
require(magrittr)

Suppose we want to analyse only the month of June, then convert the temperature variable to degrees Celsius. We can create the required data frame by combining 2 dplyr verbs we learned

june_C <- mutate(filter(airquality, Month == 6), 
                 Temp_C = (Temp-32)*(5/9))

As we add more operations, wrapping functions one inside the other becomes increasingly illegible. But, step by step would be redundant and write a lot of objects to the workspace.

Alternatively, we can use maggritr's pipe operator to link these successive operations

june_C <- airquality %>% 
    filter(Month == 6) %>%
    mutate(Temp_C = (Temp-32)*(5/9))

Advantages :

• less redundant code

• easy to read and write because functions are executed in order

The dplyr verbs become especially powerful when they are are combined using the pipe operator %>%. The following dplyr functions allow us to split our data frame into groups on which we can perform operations individually

group_by() : group data frame by a factor for downstream operations (usually summarise)

summarise() : summarise values in a data frame or in groups within the data frame with aggregation functions (e.g. min(), max(), mean(), etc…)

The group_by function is key to the Split-Apply-Combine strategy

Example: we are interested in the mean temperature and standard deviation within each month if the airquality dataset

month_sum <- airquality %>% 
      group_by(Month) %>% 
      summarise(mean_temp = mean(Temp),
                sd_temp = sd(Temp))
month_sum

## # A tibble: 5 x 3
##   Month mean_temp sd_temp
##   <int>     <dbl>   <dbl>
## 1     5      65.5    6.85
## 2     6      79.1    6.60
## 3     7      83.9    4.32
## 4     8      84.0    6.59
## 5     9      76.9    8.36

Using the ChickWeight dataset, create a summary table which displays the difference in weight between the maximum and minimum weight of each chick in the study.

Employ dplyr verbs and the %>% operator.

1. Use group_by() to divide the dataset by "Chick"
2. Use summarise() to calculate the weight gain within each group

weight_diff <- ChickWeight %>% 
      group_by(Chick) %>% 
      summarise(weight_diff = max(weight) - min(weight))
head(weight_diff)

## # A tibble: 6 x 2
##   Chick weight_diff
##   <ord>       <dbl>
## 1 18           4.00
## 2 16          16.0 
## 3 15          27.0 
## 4 13          55.0 
## 5 9           58.0 
## 6 20          76.0

Using the ChickWeight dataset, create a summary table which displays, for each diet, the average individual difference in weight between the end and the beginning of the study.

Employ dplyr verbs and the %>% operator.

(Hint: first() and last() may be useful here.)

diet_summ <- ChickWeight %>% 
      group_by(Diet, Chick) %>% 
      summarise(weight_gain = last(weight) - first(weight)) %>% 
      group_by(Diet) %>% 
      summarise(mean_gain = mean(weight_gain))
diet_summ

## # A tibble: 4 x 2
##   Diet   mean_gain
##   <fctr>     <dbl>
## 1 1            115
## 2 2            174
## 3 3            230
## 4 4            188

Learn more on dplyr

dplyr and tidyr cheatsheet